SQL Injection Jailbreak: a structural disaster of large language models

• URL: http://arxiv.org/abs/2411.01565v1
• Date: Sun, 03 Nov 2024 13:36:34 GMT
• Title: SQL Injection Jailbreak: a structural disaster of large language models
• Authors: Jiawei Zhao, Kejiang Chen, Weiming Zhang, Nenghai Yu,
• Abstract summary: We propose a novel jailbreak method, which utilizes the construction of input prompts by LLMs to inject jailbreak information into user prompts. Our SIJ method achieves nearly 100% attack success rates on five well-known open-source LLMs in the context of AdvBench.
• Score: 71.55108680517422
• License:
• Abstract: In recent years, the rapid development of large language models (LLMs) has brought new vitality to the various domains and generated substantial social and economic benefits. However, the swift advancement of LLMs has introduced new security vulnerabilities. Jailbreak, a form of attack that induces LLMs to output harmful content through carefully crafted prompts, poses a challenge to the safe and trustworthy development of LLMs. Previous jailbreak attack methods primarily exploited the internal capabilities of the model. Among them, one category leverages the model's implicit capabilities for jailbreak attacks, where the attacker is unaware of the exact reasons for the attack's success. The other category utilizes the model's explicit capabilities for jailbreak attacks, where the attacker understands the reasons for the attack's success. For example, these attacks exploit the model's abilities in coding, contextual learning, or understanding ASCII characters. However, these earlier jailbreak attacks have certain limitations, as they only exploit the inherent capabilities of the model. In this paper, we propose a novel jailbreak method, SQL Injection Jailbreak (SIJ), which utilizes the construction of input prompts by LLMs to inject jailbreak information into user prompts, enabling successful jailbreak of the LLMs. Our SIJ method achieves nearly 100\% attack success rates on five well-known open-source LLMs in the context of AdvBench, while incurring lower time costs compared to previous methods. More importantly, SIJ reveals a new vulnerability in LLMs that urgently needs to be addressed. To this end, we propose a defense method called Self-Reminder-Key and demonstrate its effectiveness through experiments. Our code is available at \href{https://github.com/weiyezhimeng/SQL-Injection-Jailbreak}{https://github.com/weiyezhimeng/SQL-Injection-Jailbreak}.

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